Hourly Electricity Consumption

Load packages

library(tidyverse)

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library(lubridate)
library(hms)

Attaching package: ‘hms’

The following object is masked from ‘package:lubridate’:

    hms

library(googlesheets4)
library(readxl)

#ppl_15mins <- ppl_15mins %>% rename(date = Date)

hourly_ppl_pivot <- ppl_15mins %>% 
  rename(date = Date) %>% 
  pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh") 

#ppl_15mins %>%  mutate(date = as_date(Date)) 

rename(ppl_15mins, date = Date)

#rm(hourly_pivot)

hourly_ppl_pivot <- hourly_ppl_pivot %>% 
  mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))

(hourly_ppl_net <- hourly_ppl_pivot %>% 
  filter(`Read Type` == "kWh Net"))

Import solar production from another notebook (EnphaseHourly.Rmd)

ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

Net + Produced = Consumedggplot(hourly_production, aes(datetime, energy_produced_Wh)) +

geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +

theme(axis.text.x = element_text(angle = 90)) +

geom_point()

# hourly_ppl_net <- hourly_ppl_net %>% mutate(date = as_date(date))

hourly_ppl_net %>% arrange(desc(date))

hourly_production %>% arrange(desc(date))

(hourly_electricity <- hourly_ppl_net %>% 
    inner_join(hourly_production, by = join_by(date,time))  %>% 
    mutate(consumed_kWh = kWh + energy_produced_Wh/1000, produced_kWh = energy_produced_Wh/1000)  %>% 
    rename(net_kWh = kWh) %>% 
    select(datetime, date, time, net_kWh, produced_kWh, consumed_kWh))

NA

ggplot(hourly_electricity, aes(x=time)) +
  geom_point(aes(y=consumed_kWh,color="red")) 

ggplot(hourly_electricity, aes(x=datetime)) +
  geom_point(aes(y=consumed_kWh,color="red")) 

hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))

electricity_by_time <- hourly_electricity %>% 
  filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(time)

electricity_by_time

ggplot(electricity_by_time, aes(x=time)) +
  geom_point(aes(y=consumed_kWh, color="Consumed")) +
  geom_point(aes(y=net_kWh, color="Net")) +
  geom_point(aes(y=produced_kWh*-1, color="Produced")) +
  labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
  scale_color_manual(values = c("red","black","green")) +
  ggtitle("Home Electricity in 15-Minute Intervals (Apr 15 - Dec 31, 2022)")

electricity_by_month_time <- hourly_electricity %>% 
  mutate(month = as_factor(month(date))) %>% 
  filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(month, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(month, time)

`summarise()` has grouped output by 'month'. You can override using the `.groups` argument.

ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=month, color=month)) +
  geom_point() +
  ggtitle("Home Electricity in 15-Minute Intervals (Apr 15 - Dec 31, 2022)") +
  ylab("Net Electricity Consumption (kWh)")

NA
NA

Example day with EV charging

#hourly_electricity

march23 <- hourly_electricity %>% 
  filter(date >= "2023-04-01" & date < "2023-04-06")

ggplot(march23) +
  geom_point(aes(datetime,net_kWh))

ggplot(march23) +
  geom_point(aes(time,net_kWh, color = factor(date)))